{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Q-Learning: A Complete Guide\n",
    "\n",
    "# Table of Contents\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "- [What is Q-Learning?](#what-is-q-learning)\n",
    "- [Where and How Q-Learning is Used](#where-and-how-q-learning-is-used)\n",
    "- [Mathematical Foundation of Q-Learning](#mathematical-foundation-of-q-learning)\n",
    "  - [Complex Original Version](#complex-original-version)\n",
    "  - [Simplified Version](#simplified-version)\n",
    "- [Step-by-Step Explanation of Q-Learning](#step-by-step-explanation-of-q-learning)\n",
    "- [Key Components of Q-Learning](#key-components-of-q-learning)\n",
    "  - [Q-Table](#q-table)\n",
    "  - [Exploration vs. Exploitation](#exploration-vs-exploitation)\n",
    "  - [Learning Rate (α)](#learning-rate-a)\n",
    "  - [Discount Factor (γ)](#discount-factor-g)\n",
    "- [Practical Example: Grid World](#practical-example-grid-world)\n",
    "  - [Setting up the Environment](#setting-up-the-environment)\n",
    "  - [Creating a Simple Environment](#creating-a-simple-environment)\n",
    "  - [Implementing the Q-Learning Algorithm](#implementing-the-q-learning-algorithm)\n",
    "  - [Q-Table Initialization and Updates](#q-table-initialization-and-updates)\n",
    "  - [Exploration vs. Exploitation Strategy](#exploration-vs-exploitation-strategy)\n",
    "  - [Running the Q-Learning Algorithm](#running-the-q-learning-algorithm)\n",
    "  - [Visualizing the Learning Process](#visualizing-the-learning-process)\n",
    "  - [Analyzing Q-Values and Optimal Policy](#analyzing-q-values-and-optimal-policy)\n",
    "- [Testing with Different Hyperparameters (Optional)](#testing-with-different-hyperparameters-optional)\n",
    "- [Applying Q-Learning to Different Environments (Cliff Walking)](#applying-q-learning-to-different-environments-cliff-walking)\n",
    "- [Common Challenges and Solutions](#common-challenges-and-solutions)\n",
    "- [Q-Learning vs. Other Reinforcement Learning Algorithms](#q-learning-vs-other-reinforcement-learning-algorithms)\n",
    "  - [Advantages of Q-Learning](#advantages-of-q-learning)\n",
    "  - [Limitations of Q-Learning](#limitations-of-q-learning)\n",
    "  - [Related Algorithms](#related-algorithms)\n",
    "- [Conclusion](#conclusion)\n",
    "\n",
    "## What is Q-Learning?\n",
    "\n",
    "Q-learning is a reinforcement learning algorithm that enables an agent to learn the optimal action to take in any given situation by discovering which actions yield the highest rewards through trial and error. It is a model-free, value-based learning algorithm which means it doesn't require a model of the environment to learn from it. Instead, it learns from the rewards it receives after taking actions in different states.\n",
    "\n",
    "The \"Q\" in Q-learning stands for \"quality\" - essentially representing how useful a given action is in gaining some future reward.\n",
    "\n",
    "## Where and How Q-Learning is Used\n",
    "\n",
    "Q-learning is widely used in:\n",
    "\n",
    "1. **Robotics**: Teaching robots how to navigate environments, pick up objects, or complete tasks\n",
    "2. **Game playing**: Creating agents that can master games (like Atari games or board games)\n",
    "3. **Resource management**: Optimizing decisions in systems like elevator control or traffic light management\n",
    "4. **Recommendation systems**: Learning user preferences to recommend products or content\n",
    "5. **Autonomous vehicles**: Helping self-driving cars make decisions\n",
    "\n",
    "Q-learning works particularly well in environments where:\n",
    "- The rules or model of the environment are unknown\n",
    "- The environment has clear states and actions\n",
    "- There's a well-defined reward signal\n",
    "- The environment is fully observable (the agent can see the complete state)\n",
    "\n",
    "## Mathematical Foundation of Q-Learning\n",
    "\n",
    "### Complex Original Version\n",
    "\n",
    "The Q-learning algorithm updates Q-values using the Bellman equation:\n",
    "\n",
    "$$Q(s_t, a_t) \\leftarrow Q(s_t, a_t) + \\alpha \\left[r_t + \\gamma \\max_{a} Q(s_{t+1}, a) - Q(s_t, a_t)\\right]$$\n",
    "\n",
    "Where:\n",
    "- $Q(s_t, a_t)$ is the Q-value for state $s_t$ and action $a_t$\n",
    "- $\\alpha$ is the learning rate (0 < $\\alpha$ ≤ 1)\n",
    "- $r_t$ is the reward received after taking action $a_t$ in state $s_t$\n",
    "- $\\gamma$ is the discount factor (0 ≤ $\\gamma$ ≤ 1) for future rewards\n",
    "- $\\max_{a} Q(s_{t+1}, a)$ is the maximum Q-value for the next state $s_{t+1}$ across all possible actions\n",
    "- The term in brackets $[r_t + \\gamma \\max_{a} Q(s_{t+1}, a) - Q(s_t, a_t)]$ is the temporal difference (TD) error\n",
    "\n",
    "### Simplified Version\n",
    "\n",
    "In simpler terms, the Q-learning update can be understood as:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "Q_{\\text{new}} = Q_{\\text{old}} + \\alpha \\left[ R + \\gamma \\max Q_{\\text{future}} - Q_{\\text{old}} \\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Or even more simply:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "Q_{\\text{new}} = Q_{\\text{old}} + \\alpha \\left[ \\text{Target} - Q_{\\text{old}} \\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Where \"Target\" is the reward plus the discounted future value.\n",
    "\n",
    "## Step-by-Step Explanation of Q-Learning\n",
    "\n",
    "1. **Initialize the Q-table**: Create a table with rows for each state and columns for each action, initially filled with zeros or random values.\n",
    "\n",
    "2. **Choose an action**: In state $s$, select an action $a$ using an exploration strategy (like epsilon-greedy).\n",
    "\n",
    "3. **Take the action**: Execute action $a$, observe the reward $r$ and the new state $s'$.\n",
    "\n",
    "4. **Update the Q-value**: Apply the Q-learning update formula to adjust the Q-value for the state-action pair.\n",
    "\n",
    "5. **Move to the next state**: Set the current state to the new state $s'$.\n",
    "\n",
    "6. **Repeat steps 2-5**: Continue this process until a terminal state is reached or a maximum number of steps is completed.\n",
    "\n",
    "7. **Repeat for multiple episodes**: Run multiple episodes to allow the agent to explore different state-action pairs and refine its Q-values.\n",
    "\n",
    "## Key Components of Q-Learning\n",
    "\n",
    "### Q-Table\n",
    "A Q-table is a lookup table where:\n",
    "- Rows represent states in the environment\n",
    "- Columns represent possible actions\n",
    "- Each cell contains a Q-value representing the expected future reward for taking that action in that state\n",
    "\n",
    "For example, in a simple grid world:\n",
    "\n",
    "| State | Up | Down | Left | Right |\n",
    "|-------|---|------|------|-------|\n",
    "| (0,0) | 0.0 | 0.0 | 0.0 | 0.0 |\n",
    "| (0,1) | 0.0 | 0.0 | 0.0 | 0.0 |\n",
    "| ... | ... | ... | ... | ... |\n",
    "\n",
    "### Exploration vs. Exploitation\n",
    "\n",
    "One of the core challenges in reinforcement learning is the balance between:\n",
    "\n",
    "- **Exploration**: Trying new actions to discover better rewards (taking risks)\n",
    "- **Exploitation**: Using what the agent already knows to maximize rewards (playing it safe)\n",
    "\n",
    "The **epsilon-greedy** strategy is commonly used to balance these:\n",
    "- With probability $\\epsilon$, choose a random action (explore)\n",
    "- With probability $1-\\epsilon$, choose the action with the highest Q-value (exploit)\n",
    "- $\\epsilon$ typically decreases over time as the agent learns more about the environment\n",
    "\n",
    "### Learning Rate (α)\n",
    "- Controls how much new information overrides old information\n",
    "- High learning rate (close to 1): Learns quickly but may become unstable\n",
    "- Low learning rate (close to 0): Learns slowly but more steadily\n",
    "- Typical values: 0.1 to 0.5\n",
    "\n",
    "### Discount Factor (γ)\n",
    "- Determines the importance of future rewards compared to immediate rewards\n",
    "- γ = 0: Agent only considers immediate rewards (short-sighted)\n",
    "- γ = 1: Agent values future rewards equally to immediate rewards (far-sighted)\n",
    "- Typical values: 0.9 to 0.99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practical Example: Grid World\n",
    "\n",
    "In the example from the notebook, Q-learning is applied to a grid world where:\n",
    "- The agent navigates a 4×4 grid\n",
    "- Terminal states are at (0,0) with reward 1 and (3,3) with reward 10\n",
    "- All other states have reward 0\n",
    "- The agent can move up, down, left, or right\n",
    "- The goal is to learn the optimal path to the highest reward\n",
    "\n",
    "As the agent explores, it updates its Q-values and gradually learns the best path through the environment. The visualization shows how the agent starts by exploring randomly and eventually converges on the optimal policy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Environment\n",
    "Import necessary libraries including NumPy for numerical operations and Matplotlib for visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np  # For numerical operations\n",
    "import matplotlib.pyplot as plt  # For visualizations\n",
    "\n",
    "# Import type hints\n",
    "from typing import List, Tuple, Dict, Optional\n",
    "\n",
    "# set seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Enable inline plotting for Jupyter Notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Simple Environment\n",
    "\n",
    "To create a simple environment for the Q-learning algorithm, we will define a 4x4 GridWorld. The GridWorld will have the following properties:\n",
    "    \n",
    "- 4 rows and 4 columns\n",
    "- Possible actions: 'up', 'down', 'left', 'right'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the GridWorld environment\n",
    "def create_gridworld(\n",
    "    rows: int, \n",
    "    cols: int, \n",
    "    terminal_states: List[Tuple[int, int]], \n",
    "    rewards: Dict[Tuple[int, int], int]\n",
    ") -> Tuple[np.ndarray, List[Tuple[int, int]], List[str]]:\n",
    "    \"\"\"\n",
    "    Create a simple GridWorld environment.\n",
    "    \n",
    "    Parameters:\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - terminal_states (List[Tuple[int, int]]): List of terminal states as (row, col) tuples.\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping (row, col) to reward values.\n",
    "    \n",
    "    Returns:\n",
    "    - grid (np.ndarray): A 2D array representing the grid with rewards.\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states in the grid.\n",
    "    - action_space (List[str]): List of possible actions ('up', 'down', 'left', 'right').\n",
    "    \"\"\"\n",
    "    # Initialize the grid with zeros\n",
    "    grid = np.zeros((rows, cols))\n",
    "    \n",
    "    # Assign rewards to specified states\n",
    "    for (row, col), reward in rewards.items():\n",
    "        grid[row, col] = reward\n",
    "    \n",
    "    # Define the state space as all possible (row, col) pairs\n",
    "    state_space = [\n",
    "        (row, col) \n",
    "        for row in range(rows) \n",
    "        for col in range(cols)\n",
    "    ]\n",
    "    \n",
    "    # Define the action space as the four possible movements\n",
    "    action_space = ['up', 'down', 'left', 'right']\n",
    "    \n",
    "    return grid, state_space, action_space\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to state transition function, which takes the current state and action as input and returns the next state. Think of this as the agent moving around the grid based on the action it takes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define state transition function\n",
    "def state_transition(state: Tuple[int, int], action: str, rows: int, cols: int) -> Tuple[int, int]:\n",
    "    \"\"\"\n",
    "    Compute the next state given the current state and action.\n",
    "    \n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - action (str): Action to take ('up', 'down', 'left', 'right').\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    \n",
    "    Returns:\n",
    "    - Tuple[int, int]: The resulting state (row, col) after taking the action.\n",
    "    \"\"\"\n",
    "    # Unpack the current state into row and column\n",
    "    row, col = state\n",
    "\n",
    "    # Update the row or column based on the action, ensuring boundaries are respected\n",
    "    if action == 'up' and row > 0:  # Move up if not in the topmost row\n",
    "        row -= 1\n",
    "    elif action == 'down' and row < rows - 1:  # Move down if not in the bottommost row\n",
    "        row += 1\n",
    "    elif action == 'left' and col > 0:  # Move left if not in the leftmost column\n",
    "        col -= 1\n",
    "    elif action == 'right' and col < cols - 1:  # Move right if not in the rightmost column\n",
    "        col += 1\n",
    "\n",
    "    # Return the new state as a tuple\n",
    "    return (row, col)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our agent can interact with the environment, we need to define the reward function. This function will return the reward for a given state, which will be used to update the Q-values during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define reward function\n",
    "def get_reward(state: Tuple[int, int], rewards: Dict[Tuple[int, int], int]) -> int:\n",
    "    \"\"\"\n",
    "    Get the reward for a given state.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping (row, col) to reward values.\n",
    "\n",
    "    Returns:\n",
    "    - int: The reward for the given state. Returns 0 if the state is not in the rewards dictionary.\n",
    "    \"\"\"\n",
    "    # Use the rewards dictionary to fetch the reward for the given state.\n",
    "    # If the state is not found, return a default reward of 0.\n",
    "    return rewards.get(state, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined the GridWorld environment and the necessary helper functions, let's test them with a simple example. We will create a 4x4 grid with two terminal states at (0, 0) and (3, 3) having rewards of 1 and 10, respectively. We will then test the state transition and reward functions by moving from the state (2, 2) upwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridWorld:\n",
      "[[ 1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.]\n",
      " [ 0.  0.  0. 10.]]\n",
      "Current State: (2, 2)\n",
      "Action Taken: up\n",
      "Next State: (1, 2)\n",
      "Reward: 0\n"
     ]
    }
   ],
   "source": [
    "# Example usage of the GridWorld environment\n",
    "\n",
    "# Define the grid dimensions (4x4), terminal states, and rewards\n",
    "rows, cols = 4, 4  # Number of rows and columns in the grid\n",
    "terminal_states = [(0, 0), (3, 3)]  # Terminal states with rewards\n",
    "rewards = {(0, 0): 1, (3, 3): 10}  # Rewards for terminal states\n",
    "\n",
    "# Create the GridWorld environment\n",
    "grid, state_space, action_space = create_gridworld(rows, cols, terminal_states, rewards)\n",
    "\n",
    "# Test the state transition and reward functions\n",
    "current_state = (2, 2)  # Starting state\n",
    "action = 'up'  # Action to take\n",
    "next_state = state_transition(current_state, action, rows, cols)  # Compute the next state\n",
    "reward = get_reward(next_state, rewards)  # Get the reward for the next state\n",
    "\n",
    "# Print the results\n",
    "print(\"GridWorld:\")  # Display the grid with rewards\n",
    "print(grid)\n",
    "print(f\"Current State: {current_state}\")  # Display the current state\n",
    "print(f\"Action Taken: {action}\")  # Display the action taken\n",
    "print(f\"Next State: {next_state}\")  # Display the resulting next state\n",
    "print(f\"Reward: {reward}\")  # Display the reward for the next state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the GridWorld environment is created with the specified dimensions, terminal states, and rewards. We randomly selected a starting state (2, 2) and an action ('up') to take. The next state is computed as (1, 2) with a reward of 0 because our terminal_states dictionary does not contain this state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing the Q-Learning Algorithm\n",
    "\n",
    "So, we have successfully implemented the GridWorld environment with state transition and reward functions. We can now use this environment to implement the Q-learning algorithm. We first need to initialize the Q-table, which is a dictionary mapping state-action pairs to Q-values. The Q-value represents the expected cumulative reward when taking a specific action in a given state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Q-table\n",
    "def initialize_q_table(state_space: List[Tuple[int, int]], action_space: List[str]) -> Dict[Tuple[int, int], Dict[str, float]]:\n",
    "    \"\"\"\n",
    "    Initialize the Q-table with zeros for all state-action pairs.\n",
    "\n",
    "    Parameters:\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states in the environment, represented as (row, col) tuples.\n",
    "    - action_space (List[str]): List of all possible actions (e.g., 'up', 'down', 'left', 'right').\n",
    "\n",
    "    Returns:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): A dictionary where each state maps to another dictionary.\n",
    "      The inner dictionary maps each action to its corresponding Q-value, initialized to 0.\n",
    "    \"\"\"\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]] = {}\n",
    "    for state in state_space:\n",
    "        # Initialize Q-values for all actions in the given state to 0\n",
    "        q_table[state] = {action: 0.0 for action in action_space}\n",
    "    return q_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the functions for choosing an action using the epsilon-greedy policy, if random values are less than epsilon, we choose a random action, otherwise, we choose the action with the highest Q-value for the current state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose action using epsilon-greedy policy\n",
    "def choose_action(state: Tuple[int, int], q_table: Dict[Tuple[int, int], Dict[str, float]], action_space: List[str], epsilon: float) -> str:\n",
    "    \"\"\"\n",
    "    Choose an action using the epsilon-greedy policy.\n",
    "\n",
    "    Parameters:\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Q-table mapping state-action pairs to Q-values.\n",
    "    - action_space (List[str]): List of possible actions (e.g., 'up', 'down', 'left', 'right').\n",
    "    - epsilon (float): Exploration rate (0 <= epsilon <= 1).\n",
    "\n",
    "    Returns:\n",
    "    - str: The chosen action.\n",
    "    \"\"\"\n",
    "    # With probability epsilon, choose a random action (exploration)\n",
    "    if np.random.rand() < epsilon:\n",
    "        return np.random.choice(action_space)\n",
    "    # Otherwise, choose the action with the highest Q-value for the current state (exploitation)\n",
    "    else:\n",
    "        return max(q_table[state], key=q_table[state].get)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we take the action and observe the reward and next state, we can update the Q-value using the Q-learning update rule. The update rule is given by:\n",
    "$$\n",
    "Q(s, a) = Q(s, a) + α * [R(s) + γ * max(Q(s', a')) - Q(s, a)]\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update Q-value\n",
    "def update_q_value(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]], \n",
    "    state: Tuple[int, int], \n",
    "    action: str, \n",
    "    reward: int, \n",
    "    next_state: Tuple[int, int], \n",
    "    alpha: float, \n",
    "    gamma: float, \n",
    "    action_space: List[str]\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Update the Q-value using the Q-learning update rule.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Q-table mapping state-action pairs to Q-values.\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - action (str): Action taken.\n",
    "    - reward (int): Reward received.\n",
    "    - next_state (Tuple[int, int]): Next state as (row, col).\n",
    "    - alpha (float): Learning rate (0 < alpha <= 1).\n",
    "    - gamma (float): Discount factor (0 <= gamma <= 1).\n",
    "    - action_space (List[str]): List of possible actions.\n",
    "\n",
    "    Returns:\n",
    "    - None: Updates the Q-table in place.\n",
    "    \"\"\"\n",
    "    # Get the maximum Q-value for the next state across all possible actions\n",
    "    max_next_q: float = max(q_table[next_state].values()) if next_state in q_table else 0.0\n",
    "\n",
    "    # Update the Q-value for the current state-action pair using the Q-learning formula\n",
    "    q_table[state][action] += alpha * (reward + gamma * max_next_q - q_table[state][action])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have defined the GridWorld environment, the state transition function, the reward function, and the Q-learning update rule. We have also implemented the Q-table initialization, action selection using the epsilon-greedy policy, and the Q-value update function. Now, we can put everything together to run multiple episodes of Q-learning in the GridWorld environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run a single episode\n",
    "def run_episode(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]], \n",
    "    state_space: List[Tuple[int, int]], \n",
    "    action_space: List[str], \n",
    "    rewards: Dict[Tuple[int, int], int], \n",
    "    rows: int, \n",
    "    cols: int, \n",
    "    alpha: float, \n",
    "    gamma: float, \n",
    "    epsilon: float, \n",
    "    max_steps: int\n",
    ") -> int:\n",
    "    \"\"\"\n",
    "    Run a single episode of Q-learning.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Q-table mapping state-action pairs to Q-values.\n",
    "    - state_space (List[Tuple[int, int]]): List of all possible states in the environment.\n",
    "    - action_space (List[str]): List of possible actions (e.g., 'up', 'down', 'left', 'right').\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping states (row, col) to reward values.\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - alpha (float): Learning rate (0 < alpha <= 1).\n",
    "    - gamma (float): Discount factor (0 <= gamma <= 1).\n",
    "    - epsilon (float): Exploration rate (0 <= epsilon <= 1).\n",
    "    - max_steps (int): Maximum number of steps allowed in the episode.\n",
    "\n",
    "    Returns:\n",
    "    - int: Total reward accumulated during the episode.\n",
    "    \"\"\"\n",
    "    # Start from a random state\n",
    "    state: Tuple[int, int] = state_space[np.random.choice(len(state_space))]\n",
    "    total_reward: int = 0  # Initialize total reward for the episode\n",
    "\n",
    "    # Loop for a maximum number of steps\n",
    "    for _ in range(max_steps):\n",
    "        # Choose an action using the epsilon-greedy policy\n",
    "        action: str = choose_action(state, q_table, action_space, epsilon)\n",
    "        \n",
    "        # Compute the next state based on the chosen action\n",
    "        next_state: Tuple[int, int] = state_transition(state, action, rows, cols)\n",
    "        \n",
    "        # Get the reward for the next state\n",
    "        reward: int = get_reward(next_state, rewards)\n",
    "        \n",
    "        # Update the Q-value for the current state-action pair\n",
    "        update_q_value(q_table, state, action, reward, next_state, alpha, gamma, action_space)\n",
    "        \n",
    "        # Accumulate the reward\n",
    "        total_reward += reward\n",
    "        \n",
    "        # Move to the next state\n",
    "        state = next_state\n",
    "        \n",
    "        # Check if the agent has reached a terminal state\n",
    "        if state in terminal_states:\n",
    "            break\n",
    "    \n",
    "    # Return the total reward accumulated during the episode\n",
    "    return total_reward\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run multiple episodes of Q-learning to train the agent in the GridWorld environment. We will track the total reward accumulated in each episode and visualize the rewards over time using a line plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set hyperparameters for the Q-learning algorithm\n",
    "alpha = 0.1  # Learning rate: Determines how much new information overrides old information\n",
    "gamma = 0.9  # Discount factor: Determines the importance of future rewards\n",
    "epsilon = 0.1  # Exploration rate: Probability of choosing a random action (exploration vs. exploitation)\n",
    "max_steps = 100  # Maximum number of steps allowed per episode\n",
    "episodes = 500  # Total number of episodes to run\n",
    "\n",
    "# Initialize the Q-table with zeros for all state-action pairs\n",
    "q_table = initialize_q_table(state_space, action_space)\n",
    "\n",
    "# List to store the total reward accumulated in each episode\n",
    "rewards_per_episode = []\n",
    "\n",
    "# Run multiple episodes of Q-learning\n",
    "for episode in range(episodes):\n",
    "    # Run a single episode and get the total reward\n",
    "    total_reward = run_episode(q_table, state_space, action_space, rewards, rows, cols, alpha, gamma, epsilon, max_steps)\n",
    "    # Append the total reward for this episode to the rewards list\n",
    "    rewards_per_episode.append(total_reward)\n",
    "\n",
    "# Adjust figure size for better visibility\n",
    "plt.figure(figsize=(20, 3))\n",
    "\n",
    "# Plot the total rewards accumulated over episodes\n",
    "plt.plot(rewards_per_episode)\n",
    "plt.xlabel('Episode')  # Label for the x-axis\n",
    "plt.ylabel('Total Reward')  # Label for the y-axis\n",
    "plt.title('Rewards Over Episodes')  # Title of the plot\n",
    "plt.show()  # Display the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observed Learning Behavior:**\n",
    "\n",
    "- **Early episodes**: Mostly zero rewards, meaning the agent is exploring.\n",
    "- **Later episodes**: High reward spikes suggest the agent finds the goal sometimes but inconsistently.\n",
    "- **Fluctuations**: Indicate the policy is not yet stable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q-Table Initialization and Updates\n",
    "Implement functions to initialize and update the Q-table based on experiences, using the Q-Learning update rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Q-table\n",
    "def initialize_q_table(state_space, action_space):\n",
    "    \"\"\"\n",
    "    Initialize the Q-table with zeros.\n",
    "    \n",
    "    Parameters:\n",
    "    - state_space: List of all possible states.\n",
    "    - action_space: List of possible actions.\n",
    "    \n",
    "    Returns:\n",
    "    - q_table: A dictionary mapping state-action pairs to Q-values.\n",
    "    \"\"\"\n",
    "    q_table = {}\n",
    "    for state in state_space:\n",
    "        q_table[state] = {action: 0 for action in action_space}\n",
    "    return q_table\n",
    "\n",
    "# Choose action using epsilon-greedy policy\n",
    "def choose_action(state, q_table, action_space, epsilon):\n",
    "    \"\"\"\n",
    "    Choose an action using the epsilon-greedy policy.\n",
    "    \n",
    "    Parameters:\n",
    "    - state: Current state as (row, col).\n",
    "    - q_table: Q-table mapping state-action pairs to Q-values.\n",
    "    - action_space: List of possible actions.\n",
    "    - epsilon: Exploration rate.\n",
    "    \n",
    "    Returns:\n",
    "    - action: The chosen action.\n",
    "    \"\"\"\n",
    "    if np.random.rand() < epsilon:\n",
    "        return np.random.choice(action_space)  # Explore\n",
    "    else:\n",
    "        return max(q_table[state], key=q_table[state].get)  # Exploit\n",
    "\n",
    "# Update Q-value\n",
    "def update_q_value(q_table, state, action, reward, next_state, alpha, gamma, action_space):\n",
    "    \"\"\"\n",
    "    Update the Q-value using the Q-learning update rule.\n",
    "    \n",
    "    Parameters:\n",
    "    - q_table: Q-table mapping state-action pairs to Q-values.\n",
    "    - state: Current state as (row, col).\n",
    "    - action: Action taken.\n",
    "    - reward: Reward received.\n",
    "    - next_state: Next state as (row, col).\n",
    "    - alpha: Learning rate.\n",
    "    - gamma: Discount factor.\n",
    "    - action_space: List of possible actions.\n",
    "    \n",
    "    Returns:\n",
    "    - None (updates q_table in place).\n",
    "    \"\"\"\n",
    "    max_next_q = max(q_table[next_state].values()) if next_state in q_table else 0\n",
    "    q_table[state][action] += alpha * (reward + gamma * max_next_q - q_table[state][action])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration vs. Exploitation Strategy\n",
    "Now to properly use the exploration vs. exploitation strategy, we need to implement it properly. The first function is the epsilon-greedy policy, which chooses an action based on the epsilon value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define epsilon-greedy policy\n",
    "def epsilon_greedy_policy(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]], \n",
    "    state: Tuple[int, int], \n",
    "    action_space: List[str], \n",
    "    epsilon: float\n",
    ") -> str:\n",
    "    \"\"\"\n",
    "    Implement the epsilon-greedy policy for action selection.\n",
    "    \n",
    "    Parameters:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Q-table mapping state-action pairs to Q-values.\n",
    "    - state (Tuple[int, int]): Current state as (row, col).\n",
    "    - action_space (List[str]): List of possible actions.\n",
    "    - epsilon (float): Exploration rate.\n",
    "    \n",
    "    Returns:\n",
    "    - str: The chosen action.\n",
    "    \"\"\"\n",
    "    # With probability epsilon, choose a random action (exploration)\n",
    "    if np.random.rand() < epsilon:\n",
    "        return np.random.choice(action_space)\n",
    "    # Otherwise, choose the action with the highest Q-value for the current state (exploitation)\n",
    "    else:\n",
    "        return max(q_table[state], key=q_table[state].get)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second function is the dynamic epsilon adjustment, which adjusts the epsilon value over time to balance exploration and exploitation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dynamic epsilon adjustment\n",
    "def adjust_epsilon(\n",
    "    initial_epsilon: float, \n",
    "    min_epsilon: float, \n",
    "    decay_rate: float, \n",
    "    episode: int\n",
    ") -> float:\n",
    "    \"\"\"\n",
    "    Dynamically adjust epsilon over time to balance exploration and exploitation.\n",
    "    \n",
    "    Parameters:\n",
    "    - initial_epsilon (float): Initial exploration rate.\n",
    "    - min_epsilon (float): Minimum exploration rate.\n",
    "    - decay_rate (float): Rate at which epsilon decays.\n",
    "    - episode (int): Current episode number.\n",
    "    \n",
    "    Returns:\n",
    "    - float: Adjusted exploration rate.\n",
    "    \"\"\"\n",
    "    # Compute the decayed epsilon value, ensuring it doesn't go below the minimum epsilon\n",
    "    return max(min_epsilon, initial_epsilon * np.exp(-decay_rate * episode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use these functions to track the epsilon values over episodes and plot the decay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example usage of epsilon-greedy policy and dynamic epsilon adjustment\n",
    "initial_epsilon: float = 1.0  # Start with full exploration\n",
    "min_epsilon: float = 0.1  # Minimum exploration rate\n",
    "decay_rate: float = 0.01  # Decay rate for epsilon\n",
    "episodes: int = 500  # Number of episodes\n",
    "\n",
    "# Track epsilon values over episodes\n",
    "epsilon_values: List[float] = []\n",
    "for episode in range(episodes):\n",
    "    # Adjust epsilon for the current episode\n",
    "    epsilon = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "    epsilon_values.append(epsilon)\n",
    "\n",
    "# Adjust figure size for better visibility\n",
    "plt.figure(figsize=(20, 3))\n",
    "\n",
    "# Plot epsilon decay over episodes\n",
    "plt.plot(epsilon_values)\n",
    "plt.xlabel('Episode')  # Label for the x-axis\n",
    "plt.ylabel('Epsilon')  # Label for the y-axis\n",
    "plt.title('Epsilon Decay Over Episodes')  # Title of the plot\n",
    "plt.show()  # Display the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Early Exploration (High Epsilon ~1.0)**  \n",
    "   - In the first **100-150 episodes**, the agent moves **randomly** due to high epsilon.  \n",
    "   - It explores various paths, including **suboptimal ones**, which is why we saw low rewards in your reward graph.  \n",
    "\n",
    "2. **Midway Transition (Epsilon Decaying)**  \n",
    "   - Around episode **150-250**, epsilon decreases, and the agent starts **favoring better actions** while still exploring.  \n",
    "   - This aligns with the **first appearance of high rewards** in your reward graph—meaning the agent **occasionally finds the goal**.  \n",
    "\n",
    "3. **Late Exploitation (Epsilon Plateauing ~0.1)**  \n",
    "   - After **250+ episodes**, epsilon is very low, meaning the agent **mostly follows the best-known path**.  \n",
    "   - Your reward graph shows **sporadic high rewards**, indicating the agent has **learned the goal but isn’t 100% consistent** yet.  \n",
    "\n",
    "**What This Means for our GridWorld Training**  \n",
    "- **Epsilon decay helped the agent learn**—it first explored widely, then refined its strategy.  \n",
    "- **Unstable high rewards suggest** the agent still struggles with finding the goal consistently.  \n",
    "- **Possible Fix:** Try a **slower epsilon decay** or an **adaptive schedule** to improve learning stability.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the Q-Learning Algorithm\n",
    "Let's run the Q-learning algorithm in the GridWorld environment and visualize the Q-values learned by the agent. We will plot the Q-values for each state-action pair over time to see how they evolve during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the Q-Learning algorithm over multiple episodes and track performance metrics\n",
    "def run_q_learning(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]], \n",
    "    state_space: List[Tuple[int, int]], \n",
    "    action_space: List[str], \n",
    "    rewards: Dict[Tuple[int, int], int], \n",
    "    rows: int, \n",
    "    cols: int, \n",
    "    alpha: float, \n",
    "    gamma: float, \n",
    "    initial_epsilon: float, \n",
    "    min_epsilon: float, \n",
    "    decay_rate: float, \n",
    "    episodes: int, \n",
    "    max_steps: int\n",
    ") -> Tuple[List[int], List[int]]:\n",
    "    \"\"\"\n",
    "    Execute the Q-Learning algorithm over multiple episodes.\n",
    "    \n",
    "    Parameters:\n",
    "    - q_table: Q-table mapping state-action pairs to Q-values.\n",
    "    - state_space: List of all possible states.\n",
    "    - action_space: List of possible actions.\n",
    "    - rewards: Dictionary mapping (row, col) to reward values.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - alpha: Learning rate.\n",
    "    - gamma: Discount factor.\n",
    "    - initial_epsilon: Initial exploration rate.\n",
    "    - min_epsilon: Minimum exploration rate.\n",
    "    - decay_rate: Rate at which epsilon decays.\n",
    "    - episodes: Number of episodes to run.\n",
    "    - max_steps: Maximum number of steps per episode.\n",
    "    \n",
    "    Returns:\n",
    "    - rewards_per_episode: List of total rewards per episode.\n",
    "    - episode_lengths: List of episode lengths.\n",
    "    \"\"\"\n",
    "    # Initialize lists to store metrics\n",
    "    rewards_per_episode: List[int] = []\n",
    "    episode_lengths: List[int] = []\n",
    "    \n",
    "    # Loop through each episode\n",
    "    for episode in range(episodes):\n",
    "        # Start from a random state\n",
    "        state: Tuple[int, int] = state_space[np.random.choice(len(state_space))]\n",
    "        total_reward: int = 0  # Initialize total reward for the episode\n",
    "        steps: int = 0  # Initialize step counter\n",
    "        # Adjust epsilon for the current episode\n",
    "        epsilon: float = adjust_epsilon(initial_epsilon, min_epsilon, decay_rate, episode)\n",
    "        \n",
    "        # Loop for a maximum number of steps\n",
    "        for _ in range(max_steps):\n",
    "            # Choose an action using the epsilon-greedy policy\n",
    "            action: str = epsilon_greedy_policy(q_table, state, action_space, epsilon)\n",
    "            # Compute the next state based on the chosen action\n",
    "            next_state: Tuple[int, int] = state_transition(state, action, rows, cols)\n",
    "            # Get the reward for the next state\n",
    "            reward: int = get_reward(next_state, rewards)\n",
    "            # Update the Q-value for the current state-action pair\n",
    "            update_q_value(q_table, state, action, reward, next_state, alpha, gamma, action_space)\n",
    "            # Accumulate the reward\n",
    "            total_reward += reward\n",
    "            # Move to the next state\n",
    "            state = next_state\n",
    "            # Increment the step counter\n",
    "            steps += 1\n",
    "            # Check if the agent has reached a terminal state\n",
    "            if state in terminal_states:\n",
    "                break\n",
    "        \n",
    "        # Append metrics for the current episode\n",
    "        rewards_per_episode.append(total_reward)\n",
    "        episode_lengths.append(steps)\n",
    "    \n",
    "    # Return the metrics\n",
    "    return rewards_per_episode, episode_lengths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we will be running the Q-Learning algorithm over `500` episodes with a maximum of `100` steps per episode. Let's execute the algorithm and visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set hyperparameters for Q-Learning\n",
    "alpha: float = 0.1  # Learning rate\n",
    "gamma: float = 0.9  # Discount factor\n",
    "initial_epsilon: float = 1.0  # Initial exploration rate\n",
    "min_epsilon: float = 0.1  # Minimum exploration rate\n",
    "decay_rate: float = 0.01  # Decay rate for epsilon\n",
    "episodes: int = 500  # Number of episodes\n",
    "max_steps: int = 100  # Maximum steps per episode\n",
    "\n",
    "# Initialize the Q-table\n",
    "q_table: Dict[Tuple[int, int], Dict[str, float]] = initialize_q_table(state_space, action_space)\n",
    "\n",
    "# Execute the Q-Learning algorithm\n",
    "rewards_per_episode, episode_lengths = run_q_learning(\n",
    "    q_table, state_space, action_space, rewards, rows, cols, alpha, gamma,\n",
    "    initial_epsilon, min_epsilon, decay_rate, episodes, max_steps\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize our training process by plotting the total rewards accumulated over episodes and the episode lengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot cumulative rewards over episodes\n",
    "plt.figure(figsize=(20, 3))\n",
    "\n",
    "# Plot total rewards per episode\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(rewards_per_episode)\n",
    "plt.xlabel('Episode')  # Label for the x-axis\n",
    "plt.ylabel('Total Reward')  # Label for the y-axis\n",
    "plt.title('Cumulative Rewards Over Episodes')  # Title of the plot\n",
    "\n",
    "# Plot episode lengths per episode\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(episode_lengths)\n",
    "plt.xlabel('Episode')  # Label for the x-axis\n",
    "plt.ylabel('Episode Length')  # Label for the y-axis\n",
    "plt.title('Episode Lengths Over Episodes')  # Title of the plot\n",
    "\n",
    "# Adjust layout and display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's break it down:\n",
    "\n",
    "**Left Graph: Cumulative Rewards Over Episodes**\n",
    "- The **total reward per episode fluctuates** early on, showing a mix of successes and failures.  \n",
    "- **Later episodes consistently achieve the maximum reward (10),** indicating that the agent has learned an optimal policy.  \n",
    "- Some low-reward episodes still appear, suggesting occasional suboptimal exploration or stochastic behavior.  \n",
    "\n",
    "**Relation to GridWorld:**\n",
    "- The agent is moving toward **(3,3), which provides a reward of 10**.\n",
    "- Initially, it explores inefficient paths, leading to varied rewards.\n",
    "- Over time, it finds **shorter, more optimal routes**.\n",
    "\n",
    "**Right Graph: Episode Lengths Over Episodes**\n",
    "- **Early episodes have longer lengths (up to 50 steps),** meaning the agent took inefficient routes.\n",
    "- As training progresses, the **episode length decreases**, indicating the agent finds the goal **faster**.\n",
    "- **Most episodes stabilize at a low step count (~3-6 steps),** meaning the agent has optimized its path.\n",
    "\n",
    "**Relation to GridWorld:**\n",
    "- The GridWorld is only **4x4 (16 states),** so an optimal policy should solve it quickly.\n",
    "- Initially, the agent took **random or exploratory moves**, increasing step count.\n",
    "- Once Q-values converge, the agent **chooses direct routes to the goal**, reducing steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Learning Process\n",
    "\n",
    "We need to visualize what actually is happening in the Q-table. We can visualize the Q-values as heatmaps for each action and the learned policy as arrows on the grid. Let's define some visualization functions to help us with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to visualize the Q-value heatmap\n",
    "def plot_q_values(q_table: Dict[Tuple[int, int], Dict[str, float]], rows: int, cols: int, action_space: List[str]) -> None:\n",
    "    \"\"\"\n",
    "    Visualize the Q-values as a heatmap for each action.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Q-table mapping state-action pairs to Q-values.\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - action_space (List[str]): List of possible actions.\n",
    "\n",
    "    Returns:\n",
    "    - None: Displays heatmaps for Q-values of each action.\n",
    "    \"\"\"\n",
    "    # Create subplots for each action\n",
    "    fig, axes = plt.subplots(1, len(action_space), figsize=(15, 5))\n",
    "    for i, action in enumerate(action_space):\n",
    "        # Initialize a grid to store Q-values for the current action\n",
    "        q_values = np.zeros((rows, cols))\n",
    "        for (row, col), actions in q_table.items():\n",
    "            q_values[row, col] = actions[action]  # Extract Q-value for the current action\n",
    "\n",
    "        # Plot the heatmap for the current action\n",
    "        ax = axes[i]\n",
    "        cax = ax.matshow(q_values, cmap='viridis')\n",
    "        fig.colorbar(cax, ax=ax)\n",
    "        ax.set_title(f\"Q-values for action: {action}\")\n",
    "        ax.set_xlabel(\"Columns\")\n",
    "        ax.set_ylabel(\"Rows\")\n",
    "\n",
    "    # Adjust layout and display the heatmaps\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to visualize the learned policy\n",
    "def plot_policy(q_table: Dict[Tuple[int, int], Dict[str, float]], rows: int, cols: int) -> None:\n",
    "    \"\"\"\n",
    "    Visualize the learned policy as arrows on the grid.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table (Dict[Tuple[int, int], Dict[str, float]]): Q-table mapping state-action pairs to Q-values.\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "\n",
    "    Returns:\n",
    "    - None: Displays the policy visualization.\n",
    "    \"\"\"\n",
    "    # Initialize a grid to store the best action for each state\n",
    "    policy_grid = np.empty((rows, cols), dtype=str)\n",
    "    action_symbols = {'up': '↑', 'down': '↓', 'left': '←', 'right': '→'}  # Symbols for actions\n",
    "\n",
    "    # Determine the best action for each state based on Q-values\n",
    "    for (row, col), actions in q_table.items():\n",
    "        best_action = max(actions, key=actions.get)  # Get the action with the highest Q-value\n",
    "        policy_grid[row, col] = action_symbols[best_action]  # Map the action to its symbol\n",
    "\n",
    "    # Plot the policy grid with increased width\n",
    "    fig, ax = plt.subplots(figsize=(16, 3))  # Increased width from 12 to 16 for more horizontal stretch\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            ax.text(j, i, policy_grid[i, j], ha='center', va='center', fontsize=14)  # Slightly larger font\n",
    "    \n",
    "    # Create a wider grid with more horizontal space\n",
    "    ax.set_xlim(-0.5, cols - 0.5)\n",
    "    ax.set_ylim(-0.5, rows - 0.5)\n",
    "    ax.matshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1)  # Add a faint background grid\n",
    "    ax.set_xticks(range(cols))\n",
    "    ax.set_yticks(range(rows))\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_title(\"Learned Policy\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Q-value heatmaps and the learned policy side by side\n",
    "fig, axes = plt.subplots(1, len(action_space) + 1, figsize=(20, 5))\n",
    "\n",
    "# Plot the Q-value heatmaps for each action\n",
    "for i, action in enumerate(action_space):\n",
    "    q_values = np.zeros((rows, cols))\n",
    "    for (row, col), actions in q_table.items():\n",
    "        q_values[row, col] = actions[action]\n",
    "    cax = axes[i].matshow(q_values, cmap='viridis')\n",
    "    fig.colorbar(cax, ax=axes[i])\n",
    "    axes[i].set_title(f\"Q-values for action: {action}\")\n",
    "    axes[i].set_xlabel(\"Columns\")\n",
    "    axes[i].set_ylabel(\"Rows\")\n",
    "\n",
    "# Plot the learned policy\n",
    "policy_grid = np.empty((rows, cols), dtype=str)\n",
    "action_symbols = {'up': '↑', 'down': '↓', 'left': '←', 'right': '→'}\n",
    "for (row, col), actions in q_table.items():\n",
    "    best_action = max(actions, key=actions.get)\n",
    "    policy_grid[row, col] = action_symbols[best_action]\n",
    "\n",
    "axes[-1].matshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1)\n",
    "for i in range(rows):\n",
    "    for j in range(cols):\n",
    "        axes[-1].text(j, i, policy_grid[i, j], ha='center', va='center', fontsize=14)\n",
    "axes[-1].set_title(\"Learned Policy\")\n",
    "axes[-1].set_xlabel(\"Columns\")\n",
    "axes[-1].set_ylabel(\"Rows\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This visualization provides **Q-values and the learned policy** for your **4x4 GridWorld**. Let’s break it down:\n",
    "\n",
    "**Q-value Heatmaps (Left Four Plots)**\n",
    "Each heatmap represents the learned Q-values for a specific action **(up, down, left, right)** at each state in the 4x4 grid.\n",
    "\n",
    "- **Higher Q-values (brighter colors) indicate preferred actions in those states.**\n",
    "- The **\"down\" and \"right\" actions have the highest Q-values**, meaning the agent prefers to move in these directions toward the goal.\n",
    "- The **bottom-right corner (goal state) has very high values in \"up\" and \"right\"**, suggesting the agent recognizes this as a valuable location.\n",
    "\n",
    "**Learned Policy (Rightmost Plot)**\n",
    "- **This shows the best action at each state** based on learned Q-values.\n",
    "- The agent follows a generally **rightward and downward movement**, which makes sense if the goal is in the bottom-right.\n",
    "- Some **upward arrows appear at certain positions**, suggesting the presence of obstacles or a penalty for specific movements.\n",
    "\n",
    "**Comparison to GridWorld**\n",
    "1. **Agent has mostly learned an efficient policy** (moving right/down toward the goal).\n",
    "2. **Some minor inconsistencies** (like a few up/left moves) may be due to:\n",
    "   - **Exploration still happening** (ε-greedy policy not fully greedy yet).\n",
    "   - **Learning rate or discount factor affecting value propagation**.\n",
    "   - **Possible obstacles or suboptimal reward structure**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Q-Values and Optimal Policy\n",
    "Let's see the optimal policy learned by the Q-learning algorithm in tabular format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State      up         down       left       right      Optimal Action \n",
      "-----------------------------------------------------------------\n",
      "(0, 0)     2.30       0.20       0.39       0.11       up             \n",
      "(0, 1)     0.59       0.07       2.59       0.14       left           \n",
      "(0, 2)     0.87       0.24       0.12       10.21      right          \n",
      "(0, 3)     0.00       17.18      0.85       1.29       down           \n",
      "(1, 0)     2.98       0.28       0.68       0.79       up             \n",
      "(1, 1)     0.34       1.51       2.36       0.23       left           \n",
      "(1, 2)     0.63       1.91       0.31       15.36      right          \n",
      "(1, 3)     1.96       22.86      2.02       4.82       down           \n",
      "(2, 0)     2.09       0.41       0.38       0.08       up             \n",
      "(2, 1)     0.25       3.11       0.29       15.87      right          \n",
      "(2, 2)     1.45       3.59       3.79       21.67      right          \n",
      "(2, 3)     8.28       26.89      7.53       12.69      down           \n",
      "(3, 0)     0.38       0.68       0.18       13.42      right          \n",
      "(3, 1)     1.33       1.85       0.59       20.41      right          \n",
      "(3, 2)     4.75       4.86       2.59       25.13      right          \n",
      "(3, 3)     19.23      2.60       0.00       0.00       up             \n"
     ]
    }
   ],
   "source": [
    "# Create a list of dictionaries to represent the Q-table data\n",
    "q_policy_data = []\n",
    "for state, actions in q_table.items():\n",
    "    # Append a dictionary for each state containing Q-values for all actions and the optimal action\n",
    "    q_policy_data.append({\n",
    "        'State': state,  # The current state (row, col)\n",
    "        'up': actions['up'],  # Q-value for the 'up' action\n",
    "        'down': actions['down'],  # Q-value for the 'down' action\n",
    "        'left': actions['left'],  # Q-value for the 'left' action\n",
    "        'right': actions['right'],  # Q-value for the 'right' action\n",
    "        'Optimal Action': max(actions, key=actions.get)  # The action with the highest Q-value\n",
    "    })\n",
    "\n",
    "# Display the Q-table data in a tabular format\n",
    "header = ['State', 'up', 'down', 'left', 'right', 'Optimal Action']  # Define the table headers\n",
    "# Print the table header with proper spacing\n",
    "print(f\"{header[0]:<10} {header[1]:<10} {header[2]:<10} {header[3]:<10} {header[4]:<10} {header[5]:<15}\")\n",
    "print(\"-\" * 65)  # Print a separator line for better readability\n",
    "\n",
    "# Iterate through the Q-table data and print each row\n",
    "for row in q_policy_data:\n",
    "    # Print the state, Q-values for all actions, and the optimal action\n",
    "    print(f\"{row['State']!s:<10} {row['up']:<10.2f} {row['down']:<10.2f} {row['left']:<10.2f} {row['right']:<10.2f} {row['Optimal Action']:<15}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Key Observations**\n",
    "1. **Different Action Preferences**  \n",
    "   - The agent still moves **right and down** in many cases but now has **more left and up movements than before**.  \n",
    "   - Notably, **state (0,0) prefers \"up\"**, while (0,1) prefers \"left,\" which might indicate obstacles or an alternative optimal route.\n",
    "  \n",
    "2. **(3,3) Prefers \"Up\" Instead of Staying Still**  \n",
    "   - If **(3,3) is a goal state**, we would expect the Q-values for other actions to be **zero** or lower due to termination.\n",
    "   - Instead, \"up\" has a very high Q-value (19.23), meaning the agent still considers movement rather than stopping.\n",
    "\n",
    "3. **Higher Q-values Overall**\n",
    "   - The **maximum Q-values have increased** (e.g., state (2,3) has a value of 26.89 for \"down\").\n",
    "   - This suggests **more learning has taken place**, possibly due to more training episodes or different hyperparameters (learning rate, discount factor, etc.).\n",
    "\n",
    "4. **Potential Issues with Exploration**\n",
    "   - Some moves seem **less intuitive**, such as (0,0) moving \"up\" rather than right.\n",
    "   - This might mean **the exploration rate (ε) is still high**, causing some randomness in action selection.\n",
    "\n",
    "**Comparison to the Policy Visualization**\n",
    "- The Q-values here should match the **policy visualization arrows** (from the heatmaps you shared earlier).  \n",
    "- However, if they **don't align perfectly**, it could mean:\n",
    "  - The **policy is still fluctuating** due to ongoing learning.\n",
    "  - There are **reward inconsistencies or obstacles** influencing Q-values.\n",
    "  - Some **actions have similar Q-values**, making tie-breaking less obvious."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing with Different Hyperparameters (Optional)\n",
    "You can experiment with different hyperparameters to see how they affect the agent's learning process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Experiment with different hyperparameters\n",
    "learning_rates = [0.1, 0.5]  # Different learning rates (alpha) to test\n",
    "discount_factors = [0.5]  # Different discount factors (gamma) to test\n",
    "exploration_rates = [1.0]  # Different initial exploration rates (epsilon) to test\n",
    "\n",
    "# Store results for comparison\n",
    "results = []\n",
    "\n",
    "# Run experiments with different hyperparameter combinations\n",
    "for alpha in learning_rates:  # Iterate over different learning rates\n",
    "    for gamma in discount_factors:  # Iterate over different discount factors\n",
    "        for initial_epsilon in exploration_rates:  # Iterate over different initial exploration rates\n",
    "            # Initialize Q-table for the current experiment\n",
    "            q_table = initialize_q_table(state_space, action_space)\n",
    "            \n",
    "            # Run Q-Learning with the current set of hyperparameters\n",
    "            rewards_per_episode, episode_lengths = run_q_learning(\n",
    "                q_table, state_space, action_space, rewards, rows, cols, alpha, gamma,\n",
    "                initial_epsilon, min_epsilon, decay_rate, episodes, max_steps\n",
    "            )\n",
    "            \n",
    "            # Store the results of the current experiment\n",
    "            results.append({\n",
    "                'alpha': alpha,  # Learning rate\n",
    "                'gamma': gamma,  # Discount factor\n",
    "                'initial_epsilon': initial_epsilon,  # Initial exploration rate\n",
    "                'rewards_per_episode': rewards_per_episode,  # Rewards collected per episode\n",
    "                'episode_lengths': episode_lengths  # Length of each episode\n",
    "            })\n",
    "\n",
    "# Create a larger figure to visualize all hyperparameter combinations\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "# Calculate the number of rows and columns for the subplot grid\n",
    "num_rows = len(learning_rates)  # Number of rows corresponds to the number of learning rates\n",
    "num_cols = len(discount_factors) * len(exploration_rates)  # Number of columns corresponds to combinations of discount factors and exploration rates\n",
    "\n",
    "# Plot the results of each experiment\n",
    "for i, result in enumerate(results):  # Iterate over all results\n",
    "    plt.subplot(num_rows, num_cols, i + 1)  # Create a subplot for each experiment\n",
    "    plt.plot(result['rewards_per_episode'])  # Plot rewards per episode\n",
    "    plt.title(f\"α={result['alpha']}, γ={result['gamma']}, ε={result['initial_epsilon']}\")  # Add a title with hyperparameter values\n",
    "    plt.xlabel('Episode')  # Label for the x-axis\n",
    "    plt.ylabel('Total Reward')  # Label for the y-axis\n",
    "\n",
    "# Adjust layout to prevent overlap and display the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Q-Learning to Different Environments (Cliff Walking)\n",
    "Let's change the environment to a Cliff Walking scenario, where the agent must navigate a grid with cliffs to reach the goal state. The agent receives a high negative reward for falling off the cliff and a positive reward for reaching the goal state. We will apply Q-Learning to this new environment and visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Cliff Walking environment \n",
    "def create_cliff_walking_env(\n",
    "    rows: int, \n",
    "    cols: int, \n",
    "    cliff_states: List[Tuple[int, int]], \n",
    "    terminal_state: Tuple[int, int], \n",
    "    rewards: Dict[Tuple[int, int], int]\n",
    ") -> Tuple[np.ndarray, List[Tuple[int, int]], List[str]]:\n",
    "    \"\"\"\n",
    "    Create a Cliff Walking environment.\n",
    "\n",
    "    Parameters:\n",
    "    - rows (int): Number of rows in the grid.\n",
    "    - cols (int): Number of columns in the grid.\n",
    "    - cliff_states (List[Tuple[int, int]]): List of cliff states as (row, col) tuples.\n",
    "    - terminal_state (Tuple[int, int]): The terminal state as a (row, col) tuple.\n",
    "    - rewards (Dict[Tuple[int, int], int]): Dictionary mapping (row, col) to reward values.\n",
    "\n",
    "    Returns:\n",
    "    - Tuple[np.ndarray, List[Tuple[int, int]], List[str]]:\n",
    "        - grid (np.ndarray): A 2D array representing the grid with rewards.\n",
    "        - state_space (List[Tuple[int, int]]): List of all possible states in the grid.\n",
    "        - action_space (List[str]): List of possible actions ('up', 'down', 'left', 'right').\n",
    "    \"\"\"\n",
    "    # Initialize the grid with zeros\n",
    "    grid = np.zeros((rows, cols))\n",
    "\n",
    "    # Assign rewards to specified states\n",
    "    for (row, col), reward in rewards.items():\n",
    "        grid[row, col] = reward\n",
    "\n",
    "    # Assign a high negative reward for cliff states\n",
    "    for row, col in cliff_states:\n",
    "        grid[row, col] = -100\n",
    "\n",
    "    # Assign a positive reward for the terminal state\n",
    "    grid[terminal_state] = 10\n",
    "\n",
    "    # Define the state space as all possible (row, col) pairs\n",
    "    state_space = [(r, c) for r in range(rows) for c in range(cols)]\n",
    "\n",
    "    # Define the action space as the four possible movements\n",
    "    action_space = ['up', 'down', 'left', 'right']\n",
    "\n",
    "    return grid, state_space, action_space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a Cliff Walking environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Cliff Walking environment\n",
    "rows, cols = 4, 12  # Dimensions of the grid (4 rows and 12 columns)\n",
    "\n",
    "# Define the cliff states (bottom row, excluding start and goal positions)\n",
    "cliff_states = [(3, c) for c in range(1, 11)]  \n",
    "\n",
    "# Define the terminal state (goal position)\n",
    "terminal_state = (3, 11)\n",
    "\n",
    "# Define the rewards for the environment\n",
    "# Reward for reaching the terminal state is 10\n",
    "rewards = {(3, 11): 10}  \n",
    "\n",
    "# Create the Cliff Walking environment using the helper function\n",
    "# This function returns the grid, state space, and action space\n",
    "cliff_grid, cliff_state_space, cliff_action_space = create_cliff_walking_env(\n",
    "    rows, cols, cliff_states, terminal_state, rewards\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the rewards, we can use the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot rewards for the Cliff Walking environment\n",
    "def plot_rewards(rewards_per_episode: List[int], ax: plt.Axes = None) -> plt.Axes:\n",
    "    \"\"\"\n",
    "    Plot the total rewards accumulated over episodes.\n",
    "\n",
    "    Parameters:\n",
    "    - rewards_per_episode (List[int]): List of total rewards per episode.\n",
    "    - ax (plt.Axes, optional): Matplotlib axis to plot on. If None, a new figure and axis are created.\n",
    "\n",
    "    Returns:\n",
    "    - plt.Axes: The Matplotlib axis containing the plot.\n",
    "    \"\"\"\n",
    "    # If no axis is provided, create a new figure and axis\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    \n",
    "    # Plot the rewards over episodes\n",
    "    ax.plot(rewards_per_episode)\n",
    "    ax.set_xlabel('Episode')  # Label for the x-axis\n",
    "    ax.set_ylabel('Total Reward')  # Label for the y-axis\n",
    "    ax.set_title('Rewards Over Episodes')  # Title of the plot\n",
    "    \n",
    "    # Return the axis for further customization if needed\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create couple of function to plot the cliff walking environment and visualize the learned policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the Cliff Walking environment\n",
    "def plot_cliff_walking_env(\n",
    "    grid: np.ndarray, \n",
    "    cliff_states: List[Tuple[int, int]], \n",
    "    terminal_state: Tuple[int, int], \n",
    "    ax: Optional[plt.Axes] = None\n",
    ") -> plt.Axes:\n",
    "    \"\"\"\n",
    "    Visualize the Cliff Walking environment.\n",
    "\n",
    "    Parameters:\n",
    "    - grid: 2D numpy array representing the grid.\n",
    "    - cliff_states: List of cliff states as (row, col) tuples.\n",
    "    - terminal_state: The terminal state as a (row, col) tuple.\n",
    "    - ax: Optional Matplotlib axis to plot on.\n",
    "\n",
    "    Returns:\n",
    "    - ax: Matplotlib axis with the environment visualization.\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 6))\n",
    "        \n",
    "    for r in range(grid.shape[0]):\n",
    "        for c in range(grid.shape[1]):\n",
    "            if (r, c) in cliff_states:\n",
    "                ax.text(c, r, 'C', ha='center', va='center', color='red', fontsize=12)\n",
    "            elif (r, c) == terminal_state:\n",
    "                ax.text(c, r, 'G', ha='center', va='center', color='green', fontsize=12)\n",
    "            elif (r, c) == (3, 0):  # Starting position\n",
    "                ax.text(c, r, 'S', ha='center', va='center', color='blue', fontsize=12)\n",
    "            else:\n",
    "                ax.text(c, r, '.', ha='center', va='center', color='black', fontsize=12)\n",
    "    \n",
    "    ax.matshow(np.zeros_like(grid), cmap='Greys', alpha=0.1)\n",
    "    ax.set_xticks(range(grid.shape[1]))\n",
    "    ax.set_yticks(range(grid.shape[0]))\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_title(\"Cliff Walking Environment\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the Q-values for each action\n",
    "def plot_q_values(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]], \n",
    "    rows: int, \n",
    "    cols: int, \n",
    "    action_space: List[str], \n",
    "    ax: Optional[plt.Axes] = None\n",
    ") -> plt.Axes:\n",
    "    \"\"\"\n",
    "    Visualize the Q-values for each action.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: Dictionary containing Q-values for each state-action pair.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - action_space: List of possible actions.\n",
    "    - ax: Optional Matplotlib axis to plot on.\n",
    "\n",
    "    Returns:\n",
    "    - ax: Matplotlib axis with the Q-values visualization.\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    \n",
    "    # Create a grid for Q-values\n",
    "    q_values = np.zeros((rows, cols))\n",
    "    \n",
    "    # For each state, find the action with the highest Q-value\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            if state in q_table:\n",
    "                q_values[r, c] = max(q_table[state].values())\n",
    "    \n",
    "    im = ax.imshow(q_values, cmap='viridis')\n",
    "    plt.colorbar(im, ax=ax, label='Max Q-Value')\n",
    "    \n",
    "    # Add state values as text\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            if (r, c) in cliff_states:\n",
    "                text_color = 'red'\n",
    "            elif (r, c) == terminal_state:\n",
    "                text_color = 'green'\n",
    "            else:\n",
    "                text_color = 'white'\n",
    "            ax.text(c, r, f\"{q_values[r, c]:.1f}\", ha='center', va='center', color=text_color)\n",
    "    \n",
    "    ax.set_xticks(range(cols))\n",
    "    ax.set_yticks(range(rows))\n",
    "    ax.set_title('Max Q-Values')\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the policy derived from the Q-table\n",
    "def plot_policy(\n",
    "    q_table: Dict[Tuple[int, int], Dict[str, float]], \n",
    "    rows: int, \n",
    "    cols: int, \n",
    "    ax: Optional[plt.Axes] = None\n",
    ") -> plt.Axes:\n",
    "    \"\"\"\n",
    "    Visualize the policy derived from the Q-table.\n",
    "\n",
    "    Parameters:\n",
    "    - q_table: Dictionary containing Q-values for each state-action pair.\n",
    "    - rows: Number of rows in the grid.\n",
    "    - cols: Number of columns in the grid.\n",
    "    - ax: Optional Matplotlib axis to plot on.\n",
    "\n",
    "    Returns:\n",
    "    - ax: Matplotlib axis with the policy visualization.\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    \n",
    "    # Define symbols for actions\n",
    "    action_symbols = {'up': '↑', 'down': '↓', 'left': '←', 'right': '→'}\n",
    "    \n",
    "    # Create a grid for the policy\n",
    "    policy_grid = np.empty((rows, cols), dtype='U1')\n",
    "    \n",
    "    # For each state, find the action with the highest Q-value\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            if state in q_table:\n",
    "                best_action = max(q_table[state], key=q_table[state].get)\n",
    "                policy_grid[r, c] = action_symbols[best_action]\n",
    "            else:\n",
    "                policy_grid[r, c] = ' '\n",
    "    \n",
    "    # Display the policy grid\n",
    "    ax.imshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1)\n",
    "    \n",
    "    # Add policy arrows as text\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            if (r, c) in cliff_states:\n",
    "                text_color = 'red'\n",
    "            elif (r, c) == terminal_state:\n",
    "                text_color = 'green'\n",
    "            else:\n",
    "                text_color = 'black'\n",
    "            ax.text(c, r, policy_grid[r, c], ha='center', va='center', color=text_color, fontsize=20)\n",
    "    \n",
    "    ax.set_xticks(range(cols))\n",
    "    ax.set_yticks(range(rows))\n",
    "    ax.set_title('Learned Policy')\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run Q-Learning on the Cliff Walking environment\n",
    "alpha = 0.1  # Learning rate\n",
    "gamma = 0.9  # Discount factor\n",
    "initial_epsilon = 1.0  # Initial exploration rate\n",
    "min_epsilon = 0.1  # Minimum exploration rate\n",
    "decay_rate = 0.01  # Decay rate for epsilon\n",
    "episodes = 500  # Number of episodes\n",
    "max_steps = 100  # Maximum steps per episode\n",
    "\n",
    "# Initialize Q-table\n",
    "cliff_q_table = initialize_q_table(cliff_state_space, cliff_action_space)\n",
    "\n",
    "# Execute Q-Learning\n",
    "cliff_rewards_per_episode, cliff_episode_lengths = run_q_learning(\n",
    "    cliff_q_table, cliff_state_space, cliff_action_space, rewards, rows, cols, alpha, gamma,\n",
    "    initial_epsilon, min_epsilon, decay_rate, episodes, max_steps\n",
    ")\n",
    "\n",
    "# Create a 2x2 grid of visualizations\n",
    "fig, axs = plt.subplots(2, 2, figsize=(20, 12))\n",
    "\n",
    "# Plot rewards in the top-left subplot\n",
    "plot_rewards(cliff_rewards_per_episode, ax=axs[0, 0])\n",
    "\n",
    "# Plot environment in the top-right subplot\n",
    "plot_cliff_walking_env(cliff_grid, cliff_states, terminal_state, ax=axs[0, 1])\n",
    "\n",
    "# Plot Q-values in the bottom-left subplot\n",
    "plot_q_values(cliff_q_table, rows, cols, cliff_action_space, ax=axs[1, 0])\n",
    "\n",
    "# Plot policy in the bottom-right subplot\n",
    "plot_policy(cliff_q_table, rows, cols, ax=axs[1, 1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Key Observations:**\n",
    "1. **Top-Left: Rewards Over Episodes**  \n",
    "   - The total reward **steadily increases**, indicating that the agent is learning a better policy over time.  \n",
    "   - There are **some sudden drops** (dips to near-zero rewards), possibly due to the agent falling off the cliff (which incurs a heavy negative reward).\n",
    "\n",
    "2. **Top-Right: Cliff Walking Grid Representation**  \n",
    "   - The environment appears to be a **4-row by 12-column grid**.  \n",
    "   - The **start (S)** is on the bottom-left.  \n",
    "   - The **goal (G)** is on the bottom-right.  \n",
    "   - The **cliff (C)**, shown in red, is along the bottom row, meaning if the agent steps there, it falls and receives a large penalty.\n",
    "\n",
    "3. **Bottom-Left: Max Q-Values Per State**  \n",
    "   - This heatmap represents the **highest Q-value for each state**.  \n",
    "   - The values **increase as we move toward the goal**, which is expected.  \n",
    "   - The **cliff region (row 3, columns 1-10) has very low or even negative values** (highlighted in red), showing it’s an area the agent should avoid.\n",
    "\n",
    "4. **Bottom-Right: Learned Policy**  \n",
    "   - This shows the **optimal action per state** using arrows.  \n",
    "   - Most arrows point **right (→) or down (↓)**, guiding the agent toward the goal.  \n",
    "   - However, in the **cliff region (bottom row)**, the arrows show a mix of left (←) and up (↑), suggesting the agent has learned to **avoid falling into the cliff**.\n",
    "\n",
    "**Analysis:**\n",
    "- The **policy is well-learned**, with the agent mostly avoiding the cliff.\n",
    "- **Exploration caused early failures**, as seen in the reward dips.\n",
    "- The **agent may have used Q-learning or SARSA** to solve this environment.  \n",
    "  - If **Q-learning**, it learns the best long-term strategy.\n",
    "  - If **SARSA**, it learns a safer policy, avoiding risky steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Challenges and Solutions\n",
    "\n",
    "**Challenge: Slow learning in large state spaces**\n",
    "\n",
    "**Solution**: There are several approaches to handle this:\n",
    "- Function approximation (using neural networks to approximate Q-values)\n",
    "- State aggregation (grouping similar states)\n",
    "- Experience replay (reusing past experiences)\n",
    "\n",
    "**Challenge: Balancing exploration and exploitation**\n",
    "\n",
    "**Solution**: \n",
    "- Start with high exploration (high ε)\n",
    "- Gradually reduce exploration over time (decay ε)\n",
    "- Use more sophisticated exploration strategies like Boltzmann exploration\n",
    "\n",
    "**Challenge: Choosing appropriate hyperparameters**\n",
    "\n",
    "**Solution**:\n",
    "- Learning rate (α): Start with 0.1-0.3\n",
    "- Discount factor (γ): Usually 0.9-0.99\n",
    "- Exploration rate (ε): Start high (0.9-1.0) and decay to a low value (0.01-0.1)\n",
    "- Systematically test different combinations to find optimal settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q-Learning vs. Other Reinforcement Learning Algorithms\n",
    "\n",
    "### Advantages of Q-Learning\n",
    "- Simple to understand and implement\n",
    "- Model-free (doesn't need to know environment dynamics)\n",
    "- Can learn optimal policy directly\n",
    "- Works well in discrete state and action spaces\n",
    "\n",
    "### Limitations of Q-Learning\n",
    "- Struggles with large or continuous state spaces\n",
    "- Can be slow to converge\n",
    "- May overestimate Q-values (addressed by Double Q-Learning)\n",
    "- Not directly applicable to continuous action spaces\n",
    "\n",
    "### Related Algorithms\n",
    "- **SARSA**: Similar to Q-learning but uses the actual next action instead of the maximum Q-value\n",
    "- **Deep Q-Network (DQN)**: Uses neural networks to approximate Q-values for large state spaces\n",
    "- **Double Q-Learning**: Addresses Q-learning's overestimation problem\n",
    "- **Q-Learning with Function Approximation**: Uses function approximation for large or continuous state spaces\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Q-learning is a powerful and intuitive reinforcement learning algorithm that has been successfully applied to many problems. Its strengths lie in its simplicity and ability to learn without a model of the environment. While it has limitations with very large state spaces, extensions like Deep Q-Networks have addressed many of these challenges.\n",
    "\n",
    "Understanding Q-learning provides a solid foundation for exploring more advanced reinforcement learning algorithms and concepts. The core ideas of Q-learning—learning values of state-action pairs through experience and using these values to make decisions—appear throughout modern reinforcement learning."
   ]
  }
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